Abstract
Promoting sustainable transportation necessitates understanding what makes people select individual travel modes. Hence, classifiers are trained to predict travel modes, such as the use of private cars vs bikes for individual journeys in the cities. In this work, we focus on parking-related factors to propose how survey data, including spatial data and origin-destination matrices of the transport model, can be transformed into features. Next, we propose how the impact of the newly proposed features on classifiers trained with different machine learning methods can be evaluated. Results of the extensive evaluation show that the features proposed in this study can significantly increase the accuracy of travel mode choice predictions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Ding, L., Yang, X.: The response of urban travel mode choice to parking fees considering travel time variability. Adv. Civ. Eng. 2020, 1–9 (2020). https://doi.org/10.1155/2020/8969202
Hagenauer, J., Helbich, M.: A comparative study of machine learning classifiers for modeling travel mode choice. Expert Syst. Appl. 78, 273–282 (2017). https://doi.org/10.1016/j.eswa.2017.01.057
Hasnine, M.S., Habib, K.N.: What about the dynamics in daily travel mode choices? A dynamic discrete choice approach for tour-based mode choice modelling. Transport Policy 71, 70–80 (2018). https://doi.org/10.1016/j.tranpol.2018.07.011
Hillel, T., Bierlaire, M., Elshafie, M.Z., **, Y.: A systematic review of machine learning classification methodologies for modelling passenger mode choice. J. Choice Model. 38, 100221 (2021). https://doi.org/10.1016/j.jocm.2020.100221
Li, M., Zou, M., Li, H.: Urban travel behavior study based on data fusion model. Elsevier Inc. (2018). https://doi.org/10.1016/B978-0-12-817026-7.00005-9
Lu, Y., Kawamura, K.: Data-mining approach to work trip mode choice analysis in Chicago, Illinois, area. Transp. Res. Rec. 2156(1), 73–80 (2010). https://doi.org/10.3141/2156-09
Salas, P., la Fuente, R.D., Astroza, S., Carrasco, J.A.: A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity. Expert Syst. Appl. 193, 116253 (2022). https://doi.org/10.1016/j.eswa.2021.116253
Tamim Kashifi, M., Jamal, A., Samim Kashefi, M., Almoshaogeh, M., Masiur Rahman, S.: Predicting the travel mode choice with interpretable machine learning techniques: a comparative study. Travel Behav. Soc. 29, 279–296 (2022). https://doi.org/10.1016/j.tbs.2022.07.003
Tenkanen, H., Toivonen, T.: Longitudinal spatial dataset on travel times and distances by different travel modes in Helsinki Region. Sci. Data 7(1), 77 (2020). https://doi.org/10.1038/s41597-020-0413-y
**e, C., Lu, J., Parkany, E.: Work travel mode choice modeling with data mining: decision trees and neural networks. Transp. Res. Rec. 1854(1), 50–61 (2003). https://doi.org/10.3141/1854-06
Acknowledgements
This research has been supported by the CoMobility project. The CoMobility benefits from a 2.05 million€ grant from Iceland, Liechtenstein and Norway through the EEA Grants. The aim of the project is to provide a package of tools and methods for the co-creation of sustainable mobility in urban spaces.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Grzenda, M., Luckner, M., Brzozowski, Ł. (2023). Quantifying Parking Difficulty with Transport and Prediction Models for Travel Mode Choice Modelling. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14077. Springer, Cham. https://doi.org/10.1007/978-3-031-36030-5_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-36030-5_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36029-9
Online ISBN: 978-3-031-36030-5
eBook Packages: Computer ScienceComputer Science (R0)